A New Blood-Based Epigenetic Diagnostic Biomarker Test (Episwitch® NST) with High Sensitivity and Positive Predictive Value for Colorectal Cancer and Precancerous Polyps

1. Introduction

Globally, colorectal cancer (CRC) is the third most common cancer type, accounting for 10% of all cancer cases. There were 1.9 million new cases and 930,000 deaths from the disease in 2020 [1]. CRC arises from the epithelial lining of the colon or rectum, often following a progression from benign adenomatous polyps to malignant carcinoma driven by genetic mutations, epigenetic alterations, and chronic inflammation [2]. More than 80% of CRC arise from adenomatous polyps and outgrowths of the lining of the colon or rectum, which are usually asymptomatic [3]. Some of the inherited genetic disorders, such as familial adenomatous polyposis and hereditary non-polyposis colon cancer, can cause CRC and are responsible for circa 5% of all CRC cases. However, 75–95% of CRC cases occur in people with little or no genetic risk [4].

CRC diagnosis is usually performed via colonoscopy and biopsy. The most common form of CRC is adenocarcinoma, constituting between 95% and 98% of all cases. Imaging such as computed tomography(CT), magnetic resonance imaging (MRI) or positron emission tomography (PET) scans is used to identify local and distal spread and to plan the optimal surgical approach [5]. Treatments for CRC include surgery, radiation therapy, chemotherapy, and immuno-oncology therapy with checkpoint inhibitors [6]. Early CRC stages (1 and 2) are confined within the wall of the colon and could be treated radically with a combined surgical and medical approach. Late stages (3 and 4) often spread widely and are not curable. The individual likelihood of survival depends on how advanced the cancer is.

In this context, screening from the age of 45 (50 in the UK) for early detection of CRC is considered an effective measure for preventing and decreasing deaths from CRC [7]. Screening for this cancer is effective for both early detection and prevention and allows diagnosis 2–3 years before the symptoms arise. Polyps often can be removed at the precancerous stage, and an effective screening programme has the potential to reduce CRC deaths by 60% [8]. Currently, the primary screening tests include colonoscopy, faecal occult blood testing (FOBT), and monitoring of cell-free DNA from CRC tumours in blood. Colonoscopy is considered a gold-standard diagnostic test for CRC, and its sensitivity is ~95%. However, up to 20% of colonoscopies are unsuccessful due to poor preparation or difficult anatomy and cancers in these patients are missed.

Furthermore, colonoscopy bears a significant risk of bleeding and bowel perforation – up to 4% each [9]. Two main non-invasive screening tests include guaiac-based or immunochemical-based detection of blood in stool, FOBT and faecal immunochemical test (FIT), respectively. They have high specificity and negative predictive value (NPV) but lower sensitivity and positive predictive value (PPV). The latest studies confirm that these tests may miss more than half of bowel cancer cases, especially at the lower stages [10]. Other screening options include virtual CT-colonoscopy and stool DNA screening testing (FIT-DNA). Colonoscopy via a CT scan is expensive, associated with radiation exposure, and cannot remove any detected abnormal growths as standard colonoscopy can. Stool FIT-DNA screening test also looks for altered DNA associated with CRC and precancerous lesions but has a high level of false positive results [9]. The UK bowel cancer screening programme includes an FOBT test every two years between the ages of 50 and 74. FOBT overdiagnosis ranges from 2.0% to 7.6%, leading to unnecessary colonoscopies (with or without biopsies), patient distress and extra costs [11].

With the advent of epigenetic research, it has become evident that epigenetic modifications like aberrant DNA methylation [12] and histone acetylation [13] are related to CRC onset. Three-dimensional chromatin conformations (CCs), as part of genomic regulatory architecture, are also potent epigenetic regulators of gene expression and cellular pathological phenotypes [14]. Long-range epigenetic alterations in CCs were found in primary CRC and circulating DNA from CRC patients [15].

We have previously developed an epigenetic assay, EpiSwitch® [16], that employs an algorithmic-based CCs analysis. Using EpiSwitch® technology, we have shown the presence of cancer-specific CCs in peripheral blood mononuclear cells (PBMCs) and primary tumours of patients with melanoma [17,18] and prostate cancer [19]. In light of the regulatory role lately attributed to systemic exosome traffic, we have used indirect co-culture experiments or conditioned media and demonstrated horizontal transfer of CCs between cultured cancer cells and monocytes without direct contact [20]. EpiSwitch®-based commercial tests are now available to diagnose prostate cancer with 94% accuracy (PSE test) [21] and response to immune checkpoint inhibitors across 14 cancers with 85% accuracy (CiRT test) [22]. Interestingly, although the anchor sites associated with 3D genomic loops are scattered throughout genomes, by linking the top prognostic biomarkers to nearby genes (within 3Kb), it is possible to learn a great deal about the underlying processes contributing to the pathology of a disease and identify potential therapeutic strategies.

In this retrospective (with partial prospective collection) case-control study, we have used n=325 full blood samples collected from n=171 patients with CRC, n=44 patients with colorectal polyps and n=110patients with ‘clear’ colonoscopy attending colorectal clinics and performed whole Genome DNA screening for CCs correlating to CRC diagnosis. Our findings suggest the presence of two Eight-marker CC signatures in whole blood that allow rapid and cost-effective diagnosis of CRC and precancerous polyps, respectively.

2. Materials and Methods

Patient Characteristics

In this retrospective case-control study with partial prospective recruitment, n=325 whole blood samples (n=110 controls (no polyp or cancer on colonoscopy), n=44 polyp and n = 171 CRC) were obtained from patients attending colorectal clinics at James Paget University Hospital, UK Hospital Sultanah Bahiyah, Malaysia and Island Hospital, Malaysia (Table 1). Inclusion criteria: clinical and histopathological diagnosis of CRC, precancerous lesion and normal colonoscopy, no prior history of any cancer, treatment naïve, and age range 18-75. A blood sample was taken prior to treatment. N=225 samples (n=68 control patients and n=157 CRC) were collected retrospectively, and N=100 patients were recruited through a prospective observational study yielding (n=42 controls, n=44 polyps, n=14 CRC).

All samples were collected at the time of diagnosis and randomly allocated for training and test cohorts. The study was approved by the UK National Research Ethics Committee and Medical Research as well as Ethics Committee Ministry of Health Malaysia, and conducted in accordance with Good Clinical Practice guidelines and the Declaration of Helsinki. All participants provided written informed consent. All data were pseudo-anonymised. All procedures and protocols were performed in accordance with the relevant guidelines and regulations.

Preparation of 3D Genomic Templates

A 5 mL full blood sample was collected from cancer patients and controls using BD Vacutainer^® plastic EDTA tubes. The tubes were frozen and stored at −80 °C. Isolation of DNA from the whole cell lysate was performed as previously described, and DNA was fixed with formaldehyde. To identify interchromatin loops, fixed chromatin was digested into fragments with TaqI restriction enzyme, and the resulting DNA strands were joined, favouring cross-linked fragments. The cross-links were reversed, and PCR was performed using the primers designed using the algorithms of the EpiSwitch® software (as described in detail in [17,18,19,23]).

3C libraries were quantified using the Quant-iTTM Picogreen dsDNA Assay kit (Invitrogen) and normalised to 5 ng/μL prior to interrogation by PCR. The EpiSwitch® Explorer arrays were performed as published previously, with the modification of only one sample being hybridised to each array slide in the Cy3 channel. EpiSwitch® Explorer arrays, based on the Agilent SureSelect array platform, allow for the highly reproducible, non-biased interrogation of ~1.1 million anchor sites for 3D genomic interactions (964,631 experimental probes and 2500 control probes).

Custom Microarray Design

Custom microarrays were designed through the EpiSwitch® software that uses a pattern recognition algorithm based on DNA sequence, which operates on Bayesian modelling and yields a probability score of whether a region is involved in long-range chromatin interactions. GRCh38 human genome assembly was annotated across ~1.1 million sites, and the potential to form long-range chromosome conformations was quantified for each region [18,19,23,24,25,26]. The most probable interactions were identified and filtered on probabilistic score and proximity to protein, long non-coding RNA, or microRNA coding sequences. Predicted interactions were limited to EpiSwitch® sites larger than 10 kb and less than 300 kb apart. Repeat masking and sequence analysis were used to ensure unique marker sequences for each interaction. The EpiSwitch® Explorer array (Agilent Technologies, Product Code X-HS-AC-02), containing 60-mer oligonucleotide probes, was designed to interrogate potential 3D genomic interactions. 964,631 experimental and 2,500 control probes were added to a 1 x 1 M CGH microarray slide design. The experimental probes were placed on the design in singlicate with the controls in groups of 250. The control probes consisted of six different EpiSwitch® interactions generated during the extraction processes and used to monitor library quality. Four external inline control probe designs were added to detect non-human (Arabidopsis thaliana) spike-in DNA during the sample labelling protocol to provide a standard curve and control for labelling. The external spike DNA consists of 400 bp ssDNA fragments from genomic regions of A. thaliana. Array-based comparisons were performed as described previously, with the modification of only one sample being hybridised to each array slide in the Cy3 channel [18,19,23,24,25,26].

Microarray Statistical Analysis

Microarray readouts were normalised by background correction and quantile normalisation using the EpiSwitch® R analytic package, which is built on the Limma and dplyr libraries. Data was corrected for batch effects using the ComBat R script. Parametric (Limma R library, Linear Regression) and non-parametric (EpiSwitch® RankProd R library) statistical methods were performed to identify 3D genomic changes that demonstrated a difference in abundance between cancers and controls. The resulting data from both procedures were further filtered based on adjusted p-value (false discovery rate (FDR) correction) and abundance scores (AS). Only 3D genomic markers with adjusted p-value ≤0.05 and AS -1.1≤ or ≥1.1 were selected. Both filtered lists from Limma and RankProd analysis were compared, and the intersection of the two lists was chosen for further processing.

Step 1

Probes are selected based on their corrected p-value FDR, which is the product of a modified linear regression model. Probes below p-value ≤ 0.1 are selected and then further reduced by their fold change (FC); probes FC have to be ≤-1.1 or ≥1.1 to be chosen for further analysis. The last filter is a coefficient of variation (CV); probes must be below ≤0.3.

Step 2

The top 250 markers from the statistical lists are selected based on their FC for selection as markers for PCR translation.

Step 3

The resultant markers from step 1, the statistically significant probes, form the basis of enrichment analysis using hypergeometric enrichment (HE). This analysis enables marker reduction from the significant probe list and, along with the markers from step 2, forms the list of probes translated onto the EpiSwitch™ PCR platform.

The statistical probes are processed by HE to determine which genetic locations have an enrichment of statistically significant probes, indicating which genetic locations are hubs of epigenetic difference.

The most significant enriched loci based on a corrected p-value are selected for probe list generation. Genetic locations below a p-value of 0.3 or 0.2 are selected. The statistical probes mapping these genetic locations, with the markers from step 2, form the high-value markers for EpiSwitch™ PCR translation.

Translation of Array-Based 3D Genomic Markers to PCR Readouts

In the discovery cohort, we analysed the leading array-derived markers using Oxford BioDynamics (OBD's) proprietary primer design software. This process aimed to pinpoint genomic locations that are appropriate for a hydrolysis probe-based real-time PCR assay [27]. Briefly, the top array-derived markers associated with diagnostic potential were filtered on fold change and adjusted p-value. PCR primer probes were ordered from Eurofins genomics as salt-free primers. The probes were designed with a 5' FAM fluorophore, 3' IABkFQ quencher and an additional internal ZEN quencher and ordered from iDT (Integrated DNA Technologies) [28]. Each assay was optimised using a temperature gradient PCR with an annealing temperature range from 58-68°C. Individual PCR assays were tested across the temperature gradient alongside negative controls, including soluble and unstructured commercial TaqMan human genomic DNA control (Life Technologies), and a TE buffer-only negative control was used. Assay performance was assessed based on Cq values, the reliability of detection, and efficiency based on the slope of the individual amplification curves. Assays that passed the quality criteria and presented reliable detection differences between cancers and controls were used to screen individual patient samples.

EpiSwitch® PCR

Each patient sample was interrogated using triplicate real-time PCR. Each reaction consisted of 50 ng of EpiSwitch® library template, 250 mM of each of the primers, 200 mM of the hydrolysis probe and a final 1X Kapa Probe Force Universal (Roche) concentration in a final 25 μL volume. The PCR cycling and data collection were performed using a CFX96 Touch Real-Time PCR detection system (Bio-Rad). The annealing temperature of each assay was changed to the optimum temperature identified in the temperature gradients performed during translation for each assay. Otherwise, the same cycling conditions were used: 98°C for 3 minutes followed by 45 cycles of 95°C for 10 seconds and 20 seconds at the identified optimum annealing temperature. The individual well Cq values were exported from the CFX manager software after baseline and threshold value checks.

PCR Statistical Analysis

The 250 markers screened on 40 individual patient samples were subject to permutated logistic modelling with bootstrapping for 500 data splits and non-parametric Rank Product analysis (EpiSwitch® RankProd R library). Two machine learning procedures (eXtreme Gradient Boosting: XGBoost and CatBoost) were used to reduce the feature pool further and identify the most predictive/prognostic 3D genomic markers. The resulting markers were then used to build the final classifying models using CatBoost and XGBoost. All analyses were performed using R statistical language with Caret, XGBoost, SHAPforxgboost and CatBoost libraries.

Biological Network/Pathway Analysis

Network analysis for functional/biological relevance of the 3D genomic markers was performed using the Hallmark Gene Sets and BioCarta and Reactome Canonical Pathway gene sets from the Molecular Signatures Database (MSigDB) [29]. Protein interaction networks were generated using the Search Tool for the Retrieval of Interacting Proteins (STRING)